Instructions: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
Input: Using the LDA model, each person in the dataset is with a topic probability vector $X_i$ . Assume $x_{ik}\in X_{i}$ denotes the likelihood that the $\emph {i}^{th}$ tweet account favors $\emph {k}^{th}$ topic in the dataset. Our topic based features can be calculated as below. Global Outlier Standard Score measures the degree that a user's tweet content is related to a certain topic compared to the other users. Local Outlier Standard Score measures the degree of interest someone shows to a certain topic by considering his own homepage content only. Three baseline classification methods: Support Vector Machines (SVM), Adaboost, and Random Forests are adopted to evaluate our extracted features. 
 Question: How do they detect spammers?
Output:
Extract features from the LDA model and use them in a binary classification task